Emily completed her M.S. in Astronomy and Astrophysics at SFSU in Summer 2026. Her research focused on developing simulation-based inference methods to reconstruct the electric fields produced by ultra-high-energy cosmic rays and neutrinos in radio air-shower detectors. Emily will begin her PhD studies at Vanderbilt University in Fall 2026.

Ryan completed his M.S. in Astronomy at SFSU in Summer 2026. His research focused on reconstructing the energy fluence of radio signals from ultra-high-energy neutrinos and cosmic rays for GRAND. He combined Bayesian noise modeling with a physics-informed graph neural network, improving the reconstruction of weak signals and reducing the need to discard low signal-to-noise events. He will start his PhD in Physics at University of Canterbury in Fall 2026.

Thomas completed his M.S. at SFSU in Summer 2026. In collaboration with Emily Weissling, he developed a simulation-based inference pipeline to reconstruct the electric fields produced by ultra-high-energy cosmic rays and neutrinos for GRAND. Thomas worked on the calibration the detector noise model and trained the neural network used to reconstruct the electric-field signals from noisy detector measurements.

Abhinav graduated from Dougherty Valley High School in San Ramon in 2026. For his senior research project, he joined my group at San Francisco State University, where he searched for evidence of new Globular Cluster candidates in gamma-ray data. He will begin an undergraduate degree in Astronomy at Yale University in Fall 2026.

Chloe graduated with honors from BASIS Independent Silicon Valley in 2026. For her senior project at San Francisco State University, she developed and validated a Bayesian neural-network framework for the probabilistic classification of Fermi-LAT gamma-ray sources, with the goal of identifying promising millisecond and young pulsar candidates while quantifying classification uncertainties. She will begin her undergraduate studies at Northwestern University in Fall 2026.

Zhisen graduated in Fall 2025. He developed and validated a deep-learning denoiser for GRAND-like radio data, showing that faint air-shower pulses can be recovered with substantially improved signal-to-noise-ratio and with more antennas retained for direction and energy reconstruction. He is now pursuing a PhD in machine learning at the University of Nebraska at Omaha.

Publication:  https://arxiv.org/abs/2602.03818

Sarvesh graduated in Fall 2025, his MSc thesis explored a new way to reconstruct the arrival directions of ultra high energy cosmic rays from radio array data. He combined air shower simulations with a physics-informed graph neural network within a simulation-based inference framework, showing that timing, amplitude, and polarization information can be folded into a single Bayesian reconstruction pipeline. He will begin his PhD at the University at Buffalo in Fall 2026.

Zach graduated in Summer 2025 and is now a staff engineer at SLAC National Accelerator Laboratory. His research developed a neural-Bayesian framework for reconstructing the directions of ultra-high-energy particles from nanosecond radio pulses with sub-degree precision, while explicitly accounting for bias. The work advances analysis methods for next-generation radio arrays such as GRAND, IceCube-Gen2, and AugerPrime Radio.

Publication: Phys.Rev.D 113 (2026) 6, 063018